{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cd89c61b-2fa7-40ca-bd4c-35322621aa3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import json\n",
    "import torch\n",
    "import sys\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import confusion_matrix, classification_report, precision_recall_fscore_support\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import DataLoader\n",
    "from tqdm import tqdm\n",
    "\n",
    "from my_dataset import MyDataSet\n",
    "from vit_model import vit_base_patch16_224_in21k as create_model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9f4e023-28c5-42ac-a228-6e7ec8dbea37",
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(data_root):\n",
    "    assert os.path.exists(data_root), f\"Dataset root: {data_root} does not exist.\"\n",
    "    test_images_path = []\n",
    "    test_images_label = []\n",
    "\n",
    "    flower_class = [cla for cla in os.listdir(data_root) if os.path.isdir(os.path.join(data_root, cla))]\n",
    "    flower_class.sort()\n",
    "    class_indices = {k: v for v, k in enumerate(flower_class)}\n",
    "    \n",
    "    with open('class_indices.json', 'w') as json_file:\n",
    "        json.dump({val: key for key, val in class_indices.items()}, json_file, indent=4)\n",
    "\n",
    "    supported = [\".jpg\", \".JPG\", \".png\", \".PNG\"]\n",
    "    for cla in flower_class:\n",
    "        cla_path = os.path.join(data_root, cla)\n",
    "        images = [os.path.join(cla_path, i) for i in os.listdir(cla_path) if os.path.splitext(i)[-1] in supported]\n",
    "        images.sort()\n",
    "        image_class = class_indices[cla]\n",
    "        test_images_path.extend(images)\n",
    "        test_images_label.extend([image_class] * len(images))\n",
    "\n",
    "    print(f\"{len(test_images_path)} images were found in the test dataset.\")\n",
    "    return test_images_path, test_images_label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f2ad819b-f62c-42f1-92e9-28e35886ab06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "    data_transform = transforms.Compose([\n",
    "        transforms.Resize(256),\n",
    "        transforms.CenterCrop(224),\n",
    "        transforms.ToTensor(),\n",
    "        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n",
    "    ])\n",
    "\n",
    "    # 加载测试集数据\n",
    "    data_root = \"CIFAR-100/test\"  # 数据集根目录\n",
    "    test_images_path, test_images_label = load_data(data_root)\n",
    "\n",
    "    # 实例化测试数据集\n",
    "    test_dataset = MyDataSet(images_path=test_images_path,\n",
    "                             images_class=test_images_label,\n",
    "                             transform=data_transform)\n",
    "\n",
    "    batch_size = 16  # 设置batch size\n",
    "    num_workers = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8])\n",
    "    test_loader = DataLoader(test_dataset,\n",
    "                             batch_size=batch_size,\n",
    "                             shuffle=False,\n",
    "                             pin_memory=True,\n",
    "                             num_workers=num_workers,\n",
    "                             collate_fn=test_dataset.collate_fn)\n",
    "\n",
    "    # 加载类别索引\n",
    "    json_path = './class_indices.json'\n",
    "    assert os.path.exists(json_path), f\"File '{json_path}' does not exist.\"\n",
    "\n",
    "    with open(json_path, \"r\") as f:\n",
    "        class_indict = json.load(f)\n",
    "\n",
    "    # 加载模型\n",
    "    model = create_model(num_classes=100, has_logits=False).to(device)\n",
    "    model_weight_path = \"./weights_15/model-14.pth\"\n",
    "    assert os.path.exists(model_weight_path), f\"Model weights '{model_weight_path}' not found.\"\n",
    "    model.load_state_dict(torch.load(model_weight_path, map_location=device))\n",
    "    model.eval()\n",
    "\n",
    "    # 执行测试集评估\n",
    "    all_predictions = []\n",
    "    all_targets = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for images, labels in test_loader:\n",
    "            images = images.to(device)\n",
    "            labels = labels.to(device)\n",
    "\n",
    "            outputs = model(images)\n",
    "            predictions = torch.argmax(outputs, dim=1)\n",
    "\n",
    "            all_predictions.extend(predictions.cpu().numpy())\n",
    "            all_targets.extend(labels.cpu().numpy())\n",
    "\n",
    "    all_predictions = np.array(all_predictions)\n",
    "    all_targets = np.array(all_targets)\n",
    "\n",
    "    # Calculate confusion matrix\n",
    "    cm = confusion_matrix(all_targets, all_predictions)\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.heatmap(cm, annot=False, fmt=\"\", cmap=\"Blues\", cbar=False, xticklabels=False, yticklabels=False)\n",
    "    plt.xlabel(\"Predicted labels\")\n",
    "    plt.ylabel(\"True labels\")\n",
    "    plt.title(\"Confusion Matrix\")\n",
    "    plt.show()\n",
    "\n",
    "    # Calculate precision, recall, F1 score for each class\n",
    "    precision, recall, f1, _ = precision_recall_fscore_support(all_targets, all_predictions)\n",
    "\n",
    "    # Plot precision, recall, F1 score for each class\n",
    "    plot_metrics_per_class(precision, recall, f1, class_indict)\n",
    "\n",
    "    # Calculate and plot weighted precision, recall, F1 score\n",
    "    precision_weighted, recall_weighted, f1_weighted, _ = precision_recall_fscore_support(all_targets, all_predictions, average='weighted')\n",
    "    plot_metrics(precision_weighted, recall_weighted, f1_weighted)\n",
    "\n",
    "    # Display classification report\n",
    "    report = classification_report(all_targets, all_predictions, target_names=list(class_indict.values()), digits=4)\n",
    "    print(report)\n",
    "\n",
    "    # Calculate accuracy\n",
    "    accuracy = np.mean(all_predictions == all_targets) * 100\n",
    "    print(f\"Accuracy: {accuracy:.2f}%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c42913bf-600d-4590-ba42-fca708ee4e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics_per_class(precision, recall, f1, class_indict):\n",
    "    metrics = ['Precision', 'Recall', 'F1 Score']\n",
    "    num_classes = len(class_indict)\n",
    "    x_pos = np.arange(num_classes)\n",
    "\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    \n",
    "    plt.bar(x_pos - 0.2, precision, 0.2, label='Precision', color='blue')\n",
    "    plt.bar(x_pos, recall, 0.2, label='Recall', color='green')\n",
    "    plt.bar(x_pos + 0.2, f1, 0.2, label='F1 Score', color='orange')\n",
    "\n",
    "    plt.xticks(x_pos, list(class_indict.values()), rotation=90)\n",
    "    plt.ylabel('Score')\n",
    "    plt.title('Precision, Recall, and F1 Score per Class')\n",
    "    plt.ylim(0, 1.1)\n",
    "    plt.legend()\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f43fb934",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_metrics(precision, recall, f1):\n",
    "    metrics = ['Precision', 'Recall', 'F1 Score']\n",
    "    values = [precision, recall, f1]\n",
    "    x_pos = np.arange(len(metrics))\n",
    "\n",
    "    plt.figure(figsize=(8, 6))\n",
    "    plt.bar(x_pos, values, align='center', alpha=0.8, color=['blue', 'green', 'orange'])\n",
    "    plt.xticks(x_pos, metrics)\n",
    "    plt.ylabel('Score')\n",
    "    plt.title('Precision, Recall, and F1 Score')\n",
    "    plt.ylim(0, 1.1)\n",
    "\n",
    "    for i, v in enumerate(values):\n",
    "        plt.text(i, v + 0.01, f'{v:.3f}', ha='center', va='bottom', fontsize=10)\n",
    "\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f41ef0c5-92a2-4603-b8d1-0adcafd7f8ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000 images were found in the test dataset.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0     0.9417    0.9700    0.9557       100\n",
      "           1     0.8796    0.9500    0.9135       100\n",
      "          10     0.6957    0.6400    0.6667       100\n",
      "          11     0.6344    0.5900    0.6114       100\n",
      "          12     0.8161    0.7100    0.7594       100\n",
      "          13     0.8537    0.7000    0.7692       100\n",
      "          14     0.9053    0.8600    0.8821       100\n",
      "          15     0.8598    0.9200    0.8889       100\n",
      "          16     0.8447    0.8700    0.8571       100\n",
      "          17     0.9213    0.8200    0.8677       100\n",
      "          18     0.8854    0.8500    0.8673       100\n",
      "          19     0.8762    0.9200    0.8976       100\n",
      "           2     0.7745    0.7900    0.7822       100\n",
      "          20     0.8857    0.9300    0.9073       100\n",
      "          21     0.8017    0.9700    0.8778       100\n",
      "          22     0.9149    0.8600    0.8866       100\n",
      "          23     0.7103    0.7600    0.7343       100\n",
      "          24     0.9474    0.9000    0.9231       100\n",
      "          25     0.8539    0.7600    0.8042       100\n",
      "          26     0.8140    0.7000    0.7527       100\n",
      "          27     0.6737    0.6400    0.6564       100\n",
      "          28     0.7982    0.8700    0.8325       100\n",
      "          29     0.7692    0.8000    0.7843       100\n",
      "           3     0.8542    0.8200    0.8367       100\n",
      "          30     0.8333    0.8000    0.8163       100\n",
      "          31     0.8571    0.8400    0.8485       100\n",
      "          32     0.8652    0.7700    0.8148       100\n",
      "          33     0.7308    0.5700    0.6404       100\n",
      "          34     0.9062    0.8700    0.8878       100\n",
      "          35     0.7108    0.5900    0.6448       100\n",
      "          36     0.8190    0.9500    0.8796       100\n",
      "          37     0.6857    0.7200    0.7024       100\n",
      "          38     0.8842    0.8400    0.8615       100\n",
      "          39     0.7540    0.9500    0.8407       100\n",
      "           4     0.7634    0.7100    0.7358       100\n",
      "          40     0.7677    0.7600    0.7638       100\n",
      "          41     0.9388    0.9200    0.9293       100\n",
      "          42     0.8750    0.7000    0.7778       100\n",
      "          43     0.8000    0.9200    0.8558       100\n",
      "          44     0.7755    0.7600    0.7677       100\n",
      "          45     0.7234    0.6800    0.7010       100\n",
      "          46     0.6522    0.7500    0.6977       100\n",
      "          47     0.6627    0.5500    0.6011       100\n",
      "          48     0.9307    0.9400    0.9353       100\n",
      "          49     0.8208    0.8700    0.8447       100\n",
      "           5     0.7778    0.9100    0.8387       100\n",
      "          50     0.6696    0.7700    0.7163       100\n",
      "          51     0.8692    0.9300    0.8986       100\n",
      "          52     0.5514    0.5900    0.5700       100\n",
      "          53     0.9615    1.0000    0.9804       100\n",
      "          54     0.8585    0.9100    0.8835       100\n",
      "          55     0.6848    0.6300    0.6562       100\n",
      "          56     0.8447    0.8700    0.8571       100\n",
      "          57     0.8641    0.8900    0.8768       100\n",
      "          58     0.8571    0.8400    0.8485       100\n",
      "          59     0.7738    0.6500    0.7065       100\n",
      "           6     0.8288    0.9200    0.8720       100\n",
      "          60     0.7358    0.7800    0.7573       100\n",
      "          61     0.8100    0.8100    0.8100       100\n",
      "          62     0.8654    0.9000    0.8824       100\n",
      "          63     0.8171    0.6700    0.7363       100\n",
      "          64     0.8851    0.7700    0.8235       100\n",
      "          65     0.7982    0.8700    0.8325       100\n",
      "          66     0.8542    0.8200    0.8367       100\n",
      "          67     0.8043    0.7400    0.7708       100\n",
      "          68     0.8505    0.9100    0.8792       100\n",
      "          69     0.8571    0.9000    0.8780       100\n",
      "           7     0.8365    0.8700    0.8529       100\n",
      "          70     0.8846    0.9200    0.9020       100\n",
      "          71     0.7387    0.8200    0.7773       100\n",
      "          72     0.6786    0.7600    0.7170       100\n",
      "          73     0.8333    0.8500    0.8416       100\n",
      "          74     0.7733    0.5800    0.6629       100\n",
      "          75     0.9293    0.9200    0.9246       100\n",
      "          76     0.8019    0.8500    0.8252       100\n",
      "          77     0.8764    0.7800    0.8254       100\n",
      "          78     0.9255    0.8700    0.8969       100\n",
      "          79     0.8791    0.8000    0.8377       100\n",
      "           8     0.9375    0.9000    0.9184       100\n",
      "          80     0.7684    0.7300    0.7487       100\n",
      "          81     0.7500    0.7500    0.7500       100\n",
      "          82     0.9691    0.9400    0.9543       100\n",
      "          83     0.9118    0.9300    0.9208       100\n",
      "          84     0.7168    0.8100    0.7606       100\n",
      "          85     0.8866    0.8600    0.8731       100\n",
      "          86     0.9388    0.9200    0.9293       100\n",
      "          87     0.8148    0.8800    0.8462       100\n",
      "          88     0.9059    0.7700    0.8324       100\n",
      "          89     0.8509    0.9700    0.9065       100\n",
      "           9     0.8349    0.9100    0.8708       100\n",
      "          90     0.7857    0.8800    0.8302       100\n",
      "          91     0.8958    0.8600    0.8776       100\n",
      "          92     0.8602    0.8000    0.8290       100\n",
      "          93     0.8602    0.8000    0.8290       100\n",
      "          94     0.8302    0.8800    0.8544       100\n",
      "          95     0.8864    0.7800    0.8298       100\n",
      "          96     0.6566    0.6500    0.6533       100\n",
      "          97     0.6970    0.9200    0.7931       100\n",
      "          98     0.7043    0.8100    0.7535       100\n",
      "          99     0.8447    0.8700    0.8571       100\n",
      "\n",
      "    accuracy                         0.8178     10000\n",
      "   macro avg     0.8195    0.8178    0.8165     10000\n",
      "weighted avg     0.8195    0.8178    0.8165     10000\n",
      "\n",
      "Accuracy: 81.78%\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  }
 ],
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